Translation systems, such as, for example, speech-to-speech translation systems can be difficult and slow to use because automatic speech recognition, understanding, and translation technologies are presently imperfect and may be prone to errors under adverse conditions, such as in noisy environments, or when a user is unaware of the contents of the system vocabulary. Performance can be somewhat enhanced by employing better signal capture technologies, such as improved microphones, employing better algorithms for training robust statistical models, and the like; however, such techniques cannot completely solve the problems with speech-to-speech translation systems.
Another approach is to train users extensively in the use of a given system. This may result in successful compensation for a mismatch between machine capabilities (such as vocabulary) and the expectations of a user. However, such extensive training is quite costly.
Several strategies have been proposed in various translation systems for addressing the potential for errors. For example, U.S. Pat. No. 6,282,507 to Horiguchi et al. discloses a method and apparatus for interactive source language expression recognition and alternative hypothesis presentation and selection. Multiple recognition hypotheses in a source language are generated in response to a spoken language input. U.S. Pat. No. 6,278,968 to Franz et al. discloses a method and apparatus for adaptive speech recognition hypothesis construction and selection in a spoken language translation system. A number of ordered recognition hypotheses are generated and a user selects from the recognition hypotheses. User review of a list of speech recognition results, with no guarantee of correct translation of the selected input, may be burdensome on the user.
In view of the foregoing, there is a need in the prior art for techniques to assist translation systems, wherein accuracy and/or convenience can be enhanced.